N. Lan

Model-based sensorimotor integration for multi-joint control: development of a virtual arm model.

An integrated, sensorimotor virtual arm (VA) model has been developed and validated for simulation studies of control of human arm movements. Realistic anatomical features of shoulder, elbow and forearm joints were captured with a graphic modeling environment, SIMM. The model included 15 musculotendon elements acting at the shoulder, elbow and forearm. Muscle actions on joints were evaluated by SIMM generated moment arms that were matched to experimentally measured profiles. The Virtual MuscleTM (VM) model contained appropriate admixture of slow and fast twitch fibers with realistic physiological properties for force production. A realistic spindle model was embedded in each VM with inputs of fascicle length, gamma static (γstat) and dynamic (γdyn) controls and outputs of primary (Ia) and secondary (II) afferents. A piecewise linear model of Golgi Tendon Organ (GTO) represented the ensemble sampling (Ib) of the total muscle force at the tendon. All model components were integrated into a Simulink block using a special software tool. The complete VA model was validated with open-loop simulation at discrete hand positions within the full range of α and γ drives to extrafusal and intrafusal muscle fibers. The model behaviors were consistent with a wide variety of physiological phenomena. Spindle afferents were effectively modulated by fusimotor drives and hand positions of the arm. These simulations validated the VA model as a computational tool for studying arm movement control. The VA model is available to researchers at website http://pt.usc.edu/cel.

Computationally efficient models of neuromuscular recruitment and mechanics.

We have improved the stability and computational efficiency of a physiologically realistic, virtual muscle (VM 3.*) model (Cheng et al 2000 J. Neurosci. Methods 101 117-30) by a simpler structure of lumped fiber types and a novel recruitment algorithm. In the new version (VM 4.0), the mathematical equations are reformulated into state-space representation and structured into a CMEX S-function in SIMULINK. A continuous recruitment scheme approximates the discrete recruitment of slow and fast motor units under physiological conditions. This makes it possible to predict force output during smooth recruitment and derecruitment without having to simulate explicitly a large number of independently recruited units. We removed the intermediate state variable, effective length (Leff), which had been introduced to model the delayed length dependency of the activation-frequency relationship, but which had little effect and could introduce instability under physiological conditions of use. Both of these changes greatly reduce the number of state variables with little loss of accuracy compared to the original VM. The performance of VM 4.0 was validated by comparison with VM 3.1.5 for both single-muscle force production and a multi-joint task. The improved VM 4.0 model is more suitable for the analysis of neural control of movements and for design of prosthetic systems to restore lost or impaired motor functions. VM 4.0 is available via the internet and includes options to use the original VM model, which remains useful for detailed simulations of single motor unit behavior.

Biomechanical couplings between elbow and forearm movements

Control of reaching task requires a strategy that takes into account biomechanical couplings in the musculoskeletal system. In a kinematic study, normal subjects were asked to reach to, grasp a cup, and bring it back. It was found that forearm rotation was coordinated in a particular manner with elbow motion in order to keep the cup in a leveled orientation. This coordination pattern may be due to the multi-axial actions of biceps muscles. A realistic model of human arm was used to examine the biomechanical constraints arising from musculature couplings in the coordination of elbow and forearm movements. Moment arm analysis indicated that biceps and brachioradialis have significant moment arms in forearm supination and pronation (S/P), which displays equilibrium point like neutral positions that vary with elbow angles. The dependency of S/P neutral positions with elbow angles in biceps is consistent with the coordination pattern observed between elbow and forearm movements during the reach and grasp in normal subjects.

Modeling Spinal Sensorimotor Control for Reach Task

The spinal sensorimotor control system executes movement instructions from the central controller in the brain that plans the task in terms of global requirements. Spinal circuits serve as a local regulator that tunes the neuromuscular apparatus to an optimal state for task execution. We hypothesize that reach tasks are controlled by a set of feedforward and feedback descending commands for trajectory and final posture, respectively. This paper presents the use of physiologically realistic models of the spinal sensorimotor system to demonstrate the feasibility of such dual control for reaching movements

An integrated package of neuromusculoskeletal modeling tools in Simulink/spl trade/

An integrated neuromusculoskeletal (NMS) modeling tool has been developed to facilitate the study of the control of movement in humans and animals. Blocks representing the skeletal linkage, sensors, muscles, and neural controllers are developed using separate software tools and integrated In the powerful simulation environment of Simulink (Mathworks Inc., USA). Musculoskeletal Modeling In Simulink (MMS) converts anatomically accurate musculoskeletal models generated by SIMM (Musculographics Inc., USA) into Simulink blocks. It also removes runtime constraints in SIMM, and allows the development of complex musculoskeletal models without writing a line of code. Virtual Muscle builds realistic Simulink models of muscle force production under physiologic and pathologic conditions. A generic muscle spindle model has also been developed to simulate the sensory output of the primary and secondary afferents. Neural control models developed by various Matlab (Mathworks Inc., USA) toolboxes can be integrated easily with these model components to build complete NMS models in an integrated environment.